Applied Intelligence

, Volume 43, Issue 3, pp 499–511 | Cite as

Multi-objective breast cancer classification by using multi-expression programming

  • Laura Dioşan
  • Anca AndreicaEmail author


Despite many years of research, breast cancer detection is still a difficult, but very important problem to be solved. An automatic diagnosis system could establish whether a mammography presents tumours or belongs to a healthy patient and could offer, in this way, a second opinion to a radiologist that tries to establish a diagnosis. We therefore propose a system that could contribute to lowering both the costs and the work of an imaging diagnosis centre of breast cancer and in addition to increase the trust level in that diagnosis. We present a multi-objective evolutionary approach based on Multi-Expression Programming—a linear Genetic Programming method—that could classify a mammogram starting from a raw image of the breast. The processed images are represented through Histogram of Oriented Gradients and Kernel Descriptors since these image features have been reported as being very efficient in the image recognition scientific community and they have not been applied to mammograms before. Numerical experiments are performed on freely available datasets consisting of normal and abnormal film-based and digital mammograms and show the efficiency of the proposed decision support system.


Multi-objective optimization Genetic programming Multi-expression programming Breast cancer 


  1. 1.
    Asuncion A, Newman D (2007) UCI machine learning repositoryGoogle Scholar
  2. 2.
    Banzhaf W (1993) Genetic programming for pedestrians. In: Forrest S (ed) Proceedings of the fifth international conference on genetic algorithms (ICGA’93). Morgan Kaufmann, San Mateo, p 628Google Scholar
  3. 3.
    Barros RC, Basgalupp MP, de Carvalho ACPLF, Freitas AA (2012) A survey of evolutionary algorithms for decision-tree induction. IEEE Trans Syst Man Cybern Part C 42(3):291–312CrossRefGoogle Scholar
  4. 4.
    Basgalupp MP, de Carvalho ACPLF, Barros RC, Ruiz DD, Freitas AA (2009) Lexicographic multi-objective evolutionary induction of decision trees. IJBIC 1(1/2):105–117CrossRefGoogle Scholar
  5. 5.
    Bhowan U, Johnston M, Zhang M (2012) Developing new fitness functions in genetic programming for classification with unbalanced data. IEEE Trans Syst Man Cybern Part B: Cybern 42(2):406–421CrossRefGoogle Scholar
  6. 6.
    Bhowan U, Johnston M, Zhang M, Yao X (2013) Evolving diverse ensembles using genetic programming for classification with unbalanced data. IEEE Trans Evol Comput 17(3):368–386CrossRefGoogle Scholar
  7. 7.
    Bhowan U, Zhang M, Johnston M (2009) Genetic programming for image classification with unbalanced data. In: Proceeding of the 24th international conference image and vision computing New Zealand, IVCNZ ’09 (Wellington, Nov. 23–25). IEEE, pp 316–321Google Scholar
  8. 8.
    Bhowan U, Zhang M, Johnston M (2009) Multi-objective genetic programming for classification with unbalanced data. In: Nicholson AE, Li X (eds) AI 2009: advances in artificial intelligence, 22nd Australasian joint conference, Melbourne, Australia, December 1–4, 2009. Proceedings. Vol 5866 of Lecture Notes in Computer Science. Springer, pp 370–380Google Scholar
  9. 9.
    Bo L, Ren X, Fox D (2010) Kernel descriptors for visual recognition. In: Lafferty JD, Williams CKI, Shawe-Taylor J, Zemel RS, Culotta A (eds) NIPS. Curran Associates, Inc, pp 244–252Google Scholar
  10. 10.
    Bot MCJ Improving induction of linear classification trees with genetic programming. In: Whitley D, Goldberg D, Cantu-Paz E, Spector L, Parmee I, Beyer H-G (eds) Proceedings of the genetic and evolutionary computation conference (GECCO-2000) (Las Vegas, Nevada, USA, 10–12 July 2000). Morgan Kaufmann, pp 403–410Google Scholar
  11. 11.
    Corne D, Knowles JD, Oates MJ (2000) The pareto envelope-based selection algorithm for multi-objective optimisation. In: Schoenauer M, Deb K, Rudolph G, Yao X, Lutton E, Guervos JJM, Schwefel H-P (eds) Parallel problem solving from nature – PPSN VI (6th PPSN’2000). Vol 1917 of Lecture Notes in Computer Science (LNCS). Springer, New York, pp 839–848Google Scholar
  12. 12.
    Cortes C, Mohri M (2003) AUC optimization vs. error rate minimization. In: Thrun S, Saul LK, Schölkopf B (eds) NIPS. MIT Press, CambridgeGoogle Scholar
  13. 13.
    Crepeau RL Genetic evolution of machine language software. In: Rosca JP (ed) Proceedings of the workshop on genetic programming: from theory to real-world applications (Tahoe City, California, USA, 9 July 1995), pp 121–134Google Scholar
  14. 14.
    Dalal N, Triggs B, Schmid C (2006) Human detection using oriented histograms of flow and appearance. In: ECCV, pp II: 428–441Google Scholar
  15. 15.
    Deb K, Pratap A, Agarwal S, Meyarivan TA (2002) Fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE-EC 6, pp 182–197Google Scholar
  16. 16.
    Eggermont J, Kok JN, Kosters WA (2004) Genetic programming for data classification: partitioning the search space. In: Proceedings of the 2004 symposium on applied computing (ACM SAC’04) (Nicosia, Cyprus, 14–17 Mar), pp 1001–1005Google Scholar
  17. 17.
    Eklund SE (2002) A massively parallel GP engine in VLSI. In: Fogel DB, El-Sharkawi MA, Yao X, Greenwood G, Iba H, Marrow P, Shackleton M (eds) Proceedings of the 2002 congress on evolutionary computation CEC2002. IEEE Press, Piscataway, pp 629–633Google Scholar
  18. 18.
    Espejo PG, Ventura S, Herrera F (2010) A survey on the application of genetic programming to classification. IEEE Trans Syst Man Cybern Part C: Appl Rev 40(2):121–144CrossRefGoogle Scholar
  19. 19.
    Ferlay J, Soerjomataram I, Ervik M, Dikshit R, Eser S, Mathers C, Rebelo M, Parkin D, Forman D, Bray F (2013) Globocan 2012 v1.0, cancer incidence and mortality worldwide: Iarc cancerbase no. 11Google Scholar
  20. 20.
    Gotzsche P, Nielsen M (2011) Screening for breast cancer with mammography. The Cochrane LibraryGoogle Scholar
  21. 21.
    Khare V, Yao X, Deb K (2003) Performance scaling of multi-objective evolutionary algorithms. In: Fonseca CM, Fleming PJ, Zitzler E, Deb K, Thiele L (eds) Evolutionary multi-criterion optimization. Second international conference, EMO 2003 (Faro, Portugal, Apr.). Lecture Notes in Computer Science, vol 2632. Springer, pp 376–390Google Scholar
  22. 22.
    Kim D (2004) Structural risk minimization on decision trees using an evolutionary multiobjective optimization. In: M. Keijzer U-M O’Reilly, Lucas SM, Costa E, Soule T (eds) Genetic programming 7th European conference, EuroGP 2004, Proceedings (Coimbra, Portugal, 5–7 Apr), vol 3003 of LNCS. Springer, pp 338– 348Google Scholar
  23. 23.
    Koza J, Poli R (2005) Genetic programming. In: Burke EK, Kendall G (eds) Search methodologies: introductory tutorials in optimization and decision support techniques, ch. 5. Springer, BerlinGoogle Scholar
  24. 24.
    Levesque J-C, Durand A, Gagné C, Sabourin R (2012) Multi-objective evolutionary optimization for generating ensembles of classifiers in the ROC space. In: Soule T, Moore JH (eds) Genetic and evolutionary computation conference, GECCO ’12, Philadelphia, PA, USA, July 7–11, 2012. ACM, pp 879– 886Google Scholar
  25. 25.
    Moura D C, Guevara-López MÁ (2013) An evaluation of image descriptors combined with clinical data for breast cancer diagnosis. Int J Comput Assist Radiol Surg 8(4):561– 574CrossRefGoogle Scholar
  26. 26.
    Nelson H, Tyne K, Naik A, Bougatsos C, Chan B, Humphrey L (2009) Screening for breast cancer: systematic evidence review update for the us preventive services task force. Ann Intern Med 10(151):727CrossRefGoogle Scholar
  27. 27.
    Nordin P (1994) A compiling genetic programming system that directly manipulates the machine code. In: Kinnear KE Jr (ed) Advances in genetic programming, ch. 14. MIT Press, Cambridge, pp 311–332Google Scholar
  28. 28.
    Oltean M, Grosan C (2003) Evolving evolutionary algorithms using multi expression programming. In: Banzhaf W, Christaller T, Dittrich P, Kim JT, Ziegler J (eds) Proceedings of European conference on artificial life: advances in artificial life. Vol 2801 of Lecture Notes in Artificial Intelligence. Springer, Berlin, pp 651–658Google Scholar
  29. 29.
    Openshaw S, Turton I (1994) Building new spatial interaction models using genetic programming. In: Fogarty TC (ed) Evolutionary computing, AISB workshopGoogle Scholar
  30. 30.
    Papagelis A, Kalles D (2001) Breeding decision trees using evolutionary techniques. In: Proc. 18th international conf. on machine learning. Morgan Kaufmann, San Francisco, pp 393–400Google Scholar
  31. 31.
    Parrott D, Li X, Ciesielski V (2005) Multi-objective techniques in genetic programming for evolving classifiers. In: 2005 IEEE congress on evolutionary computation (CEC’2005) (Edinburgh, Scotland, Sept.), vol 2. IEEE Service Center, pp 1141–1148Google Scholar
  32. 32.
    Perkis T (1994) Stack-based genetic programming. In: International conference on evolutionary computation, pp 148–153Google Scholar
  33. 33.
    Poli R, Langdon WB, McPhee NF (2008) A field guide to genetic programming. Published via and freely available at
  34. 34.
    Ramos-Pollán R, Guevara-López MÁ, Ortega CS, Díaz-Herrero G, Franco-Valiente JM, del Solar MR, de Posada NG, Vaz MAP, Loureiro J, Ramos I (2012) Discovering mammography-based machine learning classifiers for breast cancer diagnosis. J Med Syst 36(4):2259–2269CrossRefGoogle Scholar
  35. 35.
    Schölkopf B (2000) The kernel trick for distances. In: Leen TK, Dietterich TG, Tresp V (eds) NIPS. MIT Press, Cambridge, pp 301–307Google Scholar
  36. 36.
    Srinivas N, Deb K (1995) Multiobjective optimization using nondominated sorting in genetic algorithms. Evol Comput 2(3):221–248CrossRefGoogle Scholar
  37. 37.
    Suckling J, Parker J, Dance DR, Astley S, Hutt I, Boggis CRM, Ricketts I, Stamatakis E, Cerneaz N, Kok S-L, Taylor P, Betal D, Savage J (1994) The Mammographic Image Analysis Society digital mammogram database. In: Proceedings of the 2nd international workshop on digital mammography (York, England, July), pp 375–378Google Scholar
  38. 38.
    Tabar L, Vitak B, Chen T, Yen A, Cohen A, Tot T, Chiu S, Chen S, Fann J, Rosell J, Fohlin H, Smith R, Duffy S (2011) Swedish two-county trial: impact of mammographic screening on breast cancer mortality during 3 decades. Radiology 3(260):658–663CrossRefGoogle Scholar
  39. 39.
    Tanigawa T, Zhao Q (2000) A study on efficient generation of decision trees using genetic programming. In: Whitley D, Goldberg D, Cantu-Paz E, Spector L, Parmee I, Beyer H-G (eds) Proceedings of the genetic and evolutionary computation conference (GECCO-2000)(Las Vegas, Nevada, USA, 10–12 July). Morgan Kaufmann, pp 1047–1052Google Scholar
  40. 40.
    Tsakonas A, Dounias G (2002) Hierarchical classification trees using type-constrained genetic programming. In: Intelligent systems, 2002. Proceedings 2002 First International IEEE Symposium, vol 2, pp 50–54Google Scholar
  41. 41.
    Wang P, Tang K, Weise T, Tsang EPK, Yao X (2014) Multiobjective genetic programming for maximizing ROC performance. Neurocomputing 125:102–118CrossRefGoogle Scholar
  42. 42.
    Zhao H (2007) A multi-objective genetic programming approach to developing pareto optimal decision trees. Decis Support Syst 43(3):809–826CrossRefGoogle Scholar
  43. 43.
    Zitzler E, Laumanns M (2001) SPEA2: improving the strength pareto evolutionary algorithm elektronische datenGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Department of Computer ScienceBabes-Bolyai UniversityCluj-NapocaRomania

Personalised recommendations